The invention relates generally to voice authentication systems, and more particularly to a system and a method for dynamically and constantly adapting a voice print model existent in voice biometric applications.
Typically, in a voice authentication system, enrolment of a user's voice sample is done only once and authentication of the user's voice sample is done multiple times over a long duration. The enrolment process of the user's voice sample is done minimal number of times to keep the system usable and alleviate the pain to the user. Hence a lot of emphasis is placed on the initial enrolment.
In certain situations, the initial parameters extracted from the user's voice sample may not be at the optimal level. Since the extracted user's voice samples are above the user set threshold level, the enrolment data is accepted and a voice print model is built for the user. However, due to weak extracted voice samples, a weak voice print model is built for the user, resulting to have a higher false rejection rates during the authentication process.
In addition, the user's voice sample can fluctuate on several conditions such as biological ageing, several environment conditions like background noise, use of different microphones, quality of microphone etc. These fluctuations in the user's voice sample affects the authentication process resulting in increase in false rejection rates. The existing systems solve this problem by asking the users to enroll the voice sample all over again and generate a new voice print model once more to capture the change in conditions. It is often difficult and tedious process for the user to do the voice enrolment process again and again.
Therefore, a system and method is needed that automatically adapts the voice print model from time to time and correspondingly pre-processes the voice samples to eliminate the unnecessary factors affecting the optimality of the user's voice samples utilized for enrolment and authentication.